Testing and implementation of integrated and intelligent transport systems (IITS) of an electrical vehicle need many high-performance and high-precision subsystems. The existing systems confine themselves with limited features and have driving range anxiety, charging and discharging time issues, and inter- and intravehicle communication problems. The above issues are the critical barriers to the penetration of EVs with a smart grid. This paper proposes the concepts which consist of connected vehicles that exploit vehicular ad hoc network (VANET) communication, embedded system integrated with sensors which acquire the static and dynamic parameter of the electrical vehicle, and cloud integration and dig data analytics tools. Vehicle control information is generated based on machine learning-based control systems. This paper also focuses on improving the overall performance (discharge time and cycle life) of a lithium ion battery, increasing the range of the electric vehicle, enhancing the safety of the battery that acquires the static and dynamic parameter and driving pattern of the electrical vehicle, establishing vehicular ad hoc network (VANET) communication, and handling and analyzing the acquired data with the help of various artificial big data analytics techniques.

Hyperspectral microscopy in biology and minerals, unsupervised deep learning neural network denoising SRS photos: hyperspectral resolution enhancement and denoising one hyperspectral picture is enough to teach unsupervised method. An intuitive chemical species map for a lithium ore sample is produced using k -means clustering. Many researchers are now interested in biosignals. Uncertainty limits the algorithms’ capacity to evaluate these signals for further information. Even while AI systems can answer puzzles, they remain limited. Deep learning is used when machine learning is inefficient. Supervised learning needs a lot of data. Deep learning is vital in modern AI. Supervised learning requires a large labeled dataset. The selection of parameters prevents over- or underfitting. Unsupervised learning is used to overcome the challenges outlined above (performed by the clustering algorithm). To accomplish this, two processing processes were used: (1) utilizing nonlinear deep learning networks to turn data into a latent feature space ( Z ). The Kullback–Leibler divergence is used to test the objective function convergence. This article explores a novel research on hyperspectral microscopic picture using deep learning and effective unsupervised learning.

No abstract

The mouse is one of the wonderful inventions of Human-Computer Interaction (HCI) technology. Currently, wireless mouse or a Bluetooth mouse still uses devices and is not free of devices completely since it uses a battery for power and a dongle to connect it to the PC. In the proposed AI virtual mouse system, this limitation can be overcome by employing webcam or a built-in camera for capturing of hand gestures and hand tip detection using computer vision. The algorithm used in the system makes use of the machine learning algorithm. Based on the hand gestures, the computer can be controlled virtually and can perform left click, right click, scrolling functions, and computer cursor function without the use of the physical mouse. The algorithm is based on deep learning for detecting the hands. Hence, the proposed system will avoid COVID-19 spread by eliminating the human intervention and dependency of devices to control the computer.

No abstract

Recently, the widespread deployment of smart phones globally, biomedical diagnostics. Smartphone-based devices are expected to be commonly used Intelligent Health Tracking Point-of-Care (PoC) Internet of Medical Things (IoMT) applications. As a result, this paper presents a smartphone-based blood lipid data acquisition dongle for measuring blood lipid levels such as TC, HDL-C, and triglycerides (TG). Blood loss in the fingertip to manage coronary artery disease. A thin photochemical during detection. The test strip, composed of LEDs and detectors, is plugged into a small dongle where the colour switches. The intensities of the calculated reflective coefficient of the lipide determinant are indicated. The product of chromogenics. Such photochemical data acquisition data dongle was focused on smart phones. Validated and achieved a correlation coefficient above 0.843 by reference to a clinical analyzer and summed. 5.017 per unit variance coefficient (CV) for 93 blood lipid patients assessment. The photochemistry Therefore, dongle is promising for the potential treatment of IoMT chronic diseases.

No abstract

Using energy efficiency to increase the life and sustainability of wireless sensor networks (WSNs) for biomedical applications is still a challenge. Clustering has boosted energy productivity by allowing cluster heads to be categorized, but its implementation is still a challenge. Existing cluster head selection criteria start with determining acceptable cluster head locations. The cluster heads are picked from the nodes that are most closely connected with these places. This location-based paradigm incorporates challenges such as faster processing, less precise selection, and redundant node selection. The development of the sampling-based smart spider monkey optimization (SSMO) approach is addressed in this paper. If the sample population’s nodes are varied, network nodes are picked from among them. The problems with distributed nodes and cluster heads are no longer a concern. This article shows how to use an SSMO and smart CH selection to increase the lifetime and stability of WSNs. The goal of this study is to look at how cluster heads are chosen using standard SMO and sampling-based SMO for biomed applications. Low-energy adaptive clustering hierarchy centralized (LEACH-C), particle swarm optimization clustering protocol (PSO-C), and SSMO improved routing protocol measurements are compared to those obtained in homogeneous and heterogeneous settings using equivalent methodologies. In these implementations, SSMO boosts network longevity and stability periods by an estimated 12.22%, 6.92%, 32.652%, and 1.22%.